Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition
This work addresses emotion recognition from EEG signals, which is important for applications like mental health monitoring, but it appears incremental as it builds on existing graph neural network approaches with a hierarchical attention mechanism.
The paper tackled EEG emotion recognition by proposing an Adaptive Progressive Attention Graph Neural Network (APAGNN) that dynamically captures spatial relationships among brain regions, achieving superior results on SEED, SEED-IV, and MPED datasets compared to baseline methods.
In recent years, numerous neuroscientific studies demonstrate that specific areas of the brain are connected to human emotional responses, with these regions exhibiting variability across individuals and emotional states. To fully leverage these neural patterns, we propose an Adaptive Progressive Attention Graph Neural Network (APAGNN), which dynamically captures the spatial relationships among brain regions during emotional processing. The APAGNN employs three specialized experts that progressively analyze brain topology. The first expert captures global brain patterns, the second focuses on region-specific features, and the third examines emotion-related channels. This hierarchical approach enables increasingly refined analysis of neural activity. Additionally, a weight generator integrates the outputs of all three experts, balancing their contributions to produce the final predictive label. Extensive experiments conducted on SEED, SEED-IV and MPED datasets indicate that our method enhances EEG emotion recognition performance, achieving superior results compared to baseline methods.